deetech™ vs FRISS: Modern Insurance Fraud Prevention

FRISS is a well-established fraud detection platform serving the property and casualty insurance market. With real-time risk scoring, network analysis, and automated fraud detection deployed across carriers globally, FRISS has earned its place in the insurance technology stack.

deetech™ provides deep AI-generated media detection and forensic analysis purpose-built for insurance claims — a specialised capability that goes beyond FRISS’s core data-pattern strengths.

FRISS recently introduced Media Check (2025), adding image and document screening to their platform through a partnership with Verisk. This is a welcome development that validates the importance of media verification in insurance. Understanding where each platform’s capabilities overlap and diverge is critical for carriers building a comprehensive fraud defense.

FRISS: Real-Time Fraud Risk Scoring

FRISS has built a strong reputation in P&C insurance fraud detection. Their platform operates across the policy lifecycle — underwriting, claims, and SIU — providing consistent fraud risk assessment at each stage.

Core capabilities:

Real-time risk scoring. Every claim receives an automated fraud risk score at the point of first notice of loss (FNOL). Scores are based on hundreds of data points analyzed against known fraud indicators.
Network analysis. FRISS maps relationships between claimants, witnesses, service providers, legal representatives, and claims handlers. Unusual network patterns — the same repairer appearing across unrelated claims, witness overlap between claimants — surface potential fraud rings.
Underwriting fraud detection. Beyond claims, FRISS assesses applications for misrepresentation, non-disclosure, and identity fraud at the point of underwriting.
Automated SIU triage. Risk scores automatically route suspicious claims to special investigations units, reducing manual review of low-risk claims.
Text mining. Analysis of unstructured text in claims notes, adjuster comments, and correspondence for indicators of fraud.
Claims handler alerts. Real-time notifications to adjusters when a claim exhibits characteristics consistent with fraud patterns.

FRISS serves an important function: ensuring that traditional fraud signals in claims data are detected automatically and consistently. Their platform reduces reliance on individual adjuster instinct and institutional knowledge by codifying fraud indicators into automated scoring models.

FRISS Media Check: A Step Forward

In 2025, FRISS launched Media Check in partnership with Verisk, adding image and document screening to their platform. This is a meaningful addition to their fraud detection stack.

What FRISS Media Check covers:

Duplicate image detection across claims
Image metadata extraction and analysis
Image manipulation detection
PDF document integrity checks
AI deepfake detection for still images
Supported formats: JPEG, PNG, GIF, TIFF, BMP, and PDF

This is a positive development. It validates what the insurance industry has recognised: media verification is no longer optional. FRISS themselves describe Media Check as part of a layered defense — an accurate characterisation.

Where gaps remain:

FRISS Media Check addresses image and document screening, but several media fraud vectors remain outside its published scope:

Video: Dashboard camera footage, security camera recordings, and video documentation submitted as evidence require frame-by-frame analysis for deepfake face swaps, temporal manipulation, and editing artifacts. FRISS Media Check’s supported formats are image and PDF only — no video formats are listed.

Audio: Voice cloning technology can produce convincing impersonations of policyholders during phone claims. Voice authentication and audio forensic analysis do not appear in FRISS Media Check’s capabilities.

Forensic depth: FRISS describes Media Check as a screening and flagging tool. For claims that proceed to formal investigation, litigation, or regulatory proceedings, deeper forensic analysis with court-ready reporting, chain of custody documentation, and statistical confidence intervals may be required.

The broader context:

The threat landscape continues to accelerate. According to Sumsub’s 2024 Identity Fraud Report, identity fraud rates doubled globally between 2021 and 2024. Deloitte estimated that generative AI-enabled fraud could reach US$40 billion in losses in the United States by 2027. The Coalition Against Insurance Fraud estimates that insurance fraud costs over US$80 billion globally per year.

The state of deepfake fraud in insurance is clear: generative AI is making fraudulent claims evidence cheaper, faster, and more convincing to produce. Media screening is a necessary first step — but comprehensive media forensics requires depth across all media types.

Where deetech™ Fits

deetech™ provides comprehensive media forensics across all media types — images, video, audio, and documents — with forensic-grade depth designed specifically for insurance claims workflows.

deetech™‘s three-layer approach:

Automated screening. Every media item — photo, video, audio file, document — attached to a claim is scanned automatically at submission. This layer operates in seconds, checking for known AI generation signatures, metadata inconsistencies, and obvious manipulation markers.
Enhanced analysis. Items flagged by automated screening receive multi-model forensic analysis. Multiple detection techniques are applied: frequency domain analysis, GAN fingerprint detection, diffusion model signatures, environmental consistency checks, and compression artifact analysis. This runs in under a minute per item, fully automated.
Forensic investigation. High-risk items receive detailed forensic examination producing court-ready reports with chain of custody documentation, methodology disclosure, and statistical confidence intervals.

What deetech™ detects:

AI-generated images (Stable Diffusion, Midjourney, DALL-E, Flux, and other generators)
Manipulated photos (splicing, cloning, inpainting, content-aware edits)
Deepfake video (face swaps, temporal manipulation, synthetic generation)
Voice cloning and audio manipulation
AI-generated documents (synthetic invoices, fabricated certificates)
Recycled imagery (reverse matching against known fraud databases)

FRISS + deetech™: The Combined Defense

The optimal architecture for modern insurance fraud detection combines both capabilities:

FRISS provides:

Claims data pattern analysis and fraud scoring
Claimant behavior scoring
Network analysis and fraud ring detection
Underwriting risk assessment
Text analysis of claims notes
Image and document screening (via Media Check)

deetech™ provides:

Multi-modal media forensics (images, video, audio, documents)
Deep AI-generated content detection across all generative architectures
Video deepfake analysis (frame-by-frame, temporal consistency)
Voice cloning and audio manipulation detection
Forensic investigation reports for litigation and regulatory proceedings
Catastrophe event media correlation

Comparing media analysis capabilities:

Capability	FRISS (incl. Media Check)	deetech™
Staged accidents (data patterns)	✅	—
Fraud rings (network analysis)	✅	—
Serial claimants	✅	—
Image duplicate detection	✅	✅
Image manipulation detection	✅	✅
AI deepfake detection (images)	✅	✅
PDF document integrity	✅	✅
Video deepfake detection	—	✅
Voice cloning detection	—	✅
Multi-model forensic analysis	—	✅
Court-ready forensic reports	—	✅
Catastrophe event correlation	—	✅

FRISS Media Check provides valuable image and document screening that adds a meaningful layer to their data-pattern analysis. deetech™ extends media verification to all media types with forensic-grade depth designed for insurance investigation and regulatory requirements.

Integration Architecture

For carriers running FRISS, adding deetech™ is architecturally straightforward:

Parallel processing:

Claim submitted → FRISS scores the data → deetech™ analyses the media → Combined risk view
Both systems process simultaneously, adding no sequential delay to claims handling

Claims system integration:

Both FRISS and deetech™ integrate with major claims platforms (Guidewire, Duck Creek, Sapiens)
Risk scores and media authenticity results appear in the same adjuster interface
No need to choose one platform’s workflow over the other

SIU workflow:

FRISS flags claims based on data patterns → SIU investigation
deetech™ flags claims based on media analysis → SIU investigation
Combined signals — high FRISS score AND media authenticity concerns — indicate highest-priority cases

Independence:

FRISS and deetech™ operate independently. Neither requires data from the other to function.
No vendor lock-in or dependency between platforms
Either can be replaced or upgraded without affecting the other

What FRISS Carriers Should Consider

If you’re already running FRISS, you have strong protection against traditional fraud patterns. The question is whether that protection extends to AI-enabled fraud using synthetic media.

Diagnostic questions:

What percentage of your claims include video or audio evidence? For motor claims with dashcam footage, liability claims with CCTV, and claims involving recorded statements, video and audio verification matters. FRISS Media Check covers images and PDFs — but video and audio forensics require specialised detection.
How would you detect an AI-generated photo of vehicle damage? FRISS Media Check now offers image screening, which is a valuable first layer. For deeper forensic analysis — multi-model ensemble detection, frequency domain analysis, and court-ready reporting — consider whether your current tooling provides sufficient depth. A 2022 study published in Proceedings of the National Academy of Sciences found that AI-generated faces were rated as more trustworthy than real faces by human evaluators.
What’s your exposure to catastrophe event fraud? After major weather events, the volume of claims creates pressure to process quickly. This is exactly when AI-generated evidence is most likely to succeed — because manual scrutiny decreases as volume increases. FRISS handles the data patterns. Who examines the photos?
Are you seeing claims with unusually high-quality documentation? Paradoxically, AI-generated evidence often looks too good — perfectly lit, well-composed, with no metadata inconsistencies in the data record. FRISS wouldn’t flag these because the data looks clean.
What’s your regulatory exposure? APRA and ASIC are increasingly focused on AI-related risks in financial services. A carrier that cannot demonstrate media verification capability may face questions about the adequacy of their fraud controls.

The Economics

Adding media authenticity detection to an existing FRISS deployment is not a replacement cost — it’s an incremental investment covering a previously unprotected attack surface.

Cost context:

Average cost of an undetected fraudulent motor claim in Australia: A$8,000-15,000
Average cost of an undetected fraudulent property claim: A$15,000-50,000
Number of fraudulent claims using AI-generated evidence: growing rapidly, currently estimated at 3-7% of all fraudulent claims and accelerating
Cost of deetech™ per-claim screening: a fraction of a single fraudulent claim payout

The ROI calculation is straightforward: if deetech™ catches even a small percentage of AI-enabled fraudulent claims that FRISS cannot detect, the investment pays for itself within the first quarter.

For a detailed financial analysis suitable for executive decision-making, the board-level briefing on generative AI fraud provides a full investment case.

The Future of Insurance Fraud Detection

The distinction between data-pattern fraud detection (FRISS) and media authenticity verification (deetech™) will become increasingly critical as generative AI advances.

Short-term (2026-2027):

AI-generated claims photos become indistinguishable from real photos to human observers
Voice cloning quality reaches the point where phone-based claims are vulnerable
AI document generation produces convincing repair quotes and medical certificates

Medium-term (2027-2029):

AI-generated video evidence becomes a practical fraud tool
Coordinated fraud attacks combine clean data patterns (defeating FRISS-type tools) with synthetic media (defeating visual inspection)
Regulatory requirements for media verification become explicit

Long-term (2029+):

Media authenticity verification becomes a standard component of claims processing, alongside data-pattern analysis
Carriers without media verification face both financial exposure and regulatory non-compliance

The carriers that build their media authenticity capability now — while the technology gap favours defenders — will be better positioned than those that wait until the threat is acute and the regulatory mandate is explicit.

Conclusion

FRISS is an effective fraud detection platform whose core strengths — data-pattern analysis, risk scoring, and network detection — are well-proven across 300+ implementations globally. The addition of Media Check demonstrates their commitment to addressing the evolving threat landscape.

deetech™ provides deeper, multi-modal media forensics purpose-built for insurance: video and audio analysis that FRISS Media Check does not yet cover, forensic-grade reporting for litigation and regulatory proceedings, and catastrophe event correlation across claims media.

For carriers running FRISS, the question is whether image and document screening alone provides sufficient media verification depth, or whether the full spectrum of media types — including video evidence, voice recordings, and forensic-grade investigation — requires a dedicated solution.

The deepfake detection FAQ for insurance companies covers the most common questions carriers ask. For comparison with other tools in the market, see the top deepfake detection tools for insurance in 2026.

To learn how deetech™ helps insurers detect deepfake fraud with purpose-built AI detection, visit our solutions page or request a demo.